def test_prepare(self):
d = Distribution(["A", "B", "C", "D"], [3, 3, -1, 0])
d.prepare()
self.assertEqual(d.getNormalizingConstant(), 6)
self.assertEqual(d["A"], 0.5)
self.assertEqual(d["B"], 0.5)
self.assertEqual(d["C"], 0.0)
self.assertEqual(d["D"], 0.0)
def test_constructor(self):
d = Distribution(["A", "B"], [0.3, 0.7])
self.assertEqual(d["A"], 0.3)
self.assertEqual(d["B"], 0.7)
d = Distribution(dictionary={"A": 0.3, "B": 0.7})
self.assertEqual(d["A"], 0.3)
self.assertEqual(d["B"], 0.7)
def test_isub(self):
# TODO: Include bigger q in this test
p = Distribution(["A", "B", "C"], [0.4, 0.3, 0.3])
q = Distribution(["A", "C", "D"], [0.4, 0.2, 0.25])
p -= q
self.assertEqual(p["A"], 0.0)
assert_allclose(p["B"], [0.3])
assert_allclose(p["C"], [0.1])
def test_prepare(self):
d = Distribution(["A", "B", "C", "D"], [3, 3, -1, 0])
d.prepare()
self.assertEqual(d.getNormalizingConstant(), 6)
self.assertEqual(d["A"], 0.5)
self.assertEqual(d["B"], 0.5)
self.assertEqual(d["C"], 0.0)
self.assertEqual(d["D"], 0.0)
def test_add(self):
p = Distribution(["A", "B"], [0.2, 0.25])
q = Distribution(["A", "C", "D"], [0.05, 0.25, 0.25])
r = p + q
self.assertEqual(r["A"], 0.25)
self.assertEqual(r["B"], 0.25)
self.assertEqual(r["C"], 0.25)
self.assertEqual(r["D"], 0.25)
def test_kld(self):
p = Distribution(["A", "B"], [0.5, 0.5])
q = Distribution(["A", "C"], [0.5, 0.5])
assert_allclose(kld(p, q), [0])
assert_allclose(kld(q, p), [0])
p = Distribution(["A", "B"], [0.3, 0.7])
q = Distribution(["A", "B"], [0.5, 0.5])
self.assertNotEqual(kld(p, q), kld(q, p))
def test_compute(self):
p = Distribution(["A", "B"], [0.3, 0.7])
q = Distribution(["A", "B", "C"], [0.3, 0.3, 0.4])
r = Distribution(["D", "C"], [0.5, 0.5])
l = [p, q, r]
for w in compute(l, [2, 1, 3], 1, logOnScreen=False):
pass
for w in compute(l, [2, 1, 3], 3, logOnScreen=False):
pass
def test_indexD(self):
p = Distribution(["A", "B"], [0.5, 0.5])
q = Distribution(["A", "C"], [0.5, 0.5])
r = Distribution(["D", "C"], [0.5, 0.5])
assert_allclose(indexD(p, 5, q, 5), [0.5])
assert_allclose(indexD(p, 5, p, 5), [0])
assert_allclose(indexD(p, 5, p, 3), [0])
assert_allclose(indexD(p, 1, p, 3), [0])
assert_allclose(indexD(p, 5, r, 5), [1])
assert_allclose(indexD(p, 5, r, 2), [1])
assert_allclose(indexD(p, 5, r, 9), [1])
def test_common(self):
p = Distribution(["A", "B", "C"], [0.3, 0.3, 0.4])
q = Distribution(["A", "B"], [0.3, 0.7])
r = common(p, q)
self.assertEqual(len(r), 2)
self.assertEqual(r["A"], 0.5)
self.assertEqual(r["B"], 0.5)
r = common(q, p)
self.assertEqual(len(r), 2)
self.assertEqual(r["A"], 0.3)
self.assertEqual(r["B"], 0.7)
def test_getsetdel(self):
d = Distribution()
d["A"] = 0.2
self.assertEqual(d["A"], 0.2)
d["A"] = 0.3
self.assertEqual(d["A"], 0.3)
del d["A"]
# with self.assertRaises(KeyError):
# d["A"]
with self.assertRaises(TypeError):
d[12]
def test_iteration(self):
d = Distribution(["A", "B"], [0.3, 0.7])
it = iter(d)
e, p = it.next()
self.assertEqual(e, "A")
self.assertEqual(p, 0.3)
e, p = it.next()
self.assertEqual(e, "B")
self.assertEqual(p, 0.7)
for e, p in d:
pass
it = iter(d)
e, p = it.next()
self.assertEqual(e, "A")
self.assertEqual(p, 0.3)
e, p = it.next()
self.assertEqual(e, "B")
self.assertEqual(p, 0.7)
def test_isEmpty(self):
d = Distribution(["A", "B"], [0.3, 0.7])
self.assertFalse(d.isEmpty())
d = Distribution()
self.assertTrue(d.isEmpty())
def test_plot(self):
p = Distribution(["A", "B", "C"], [0.3, 0.3, 0.4])
q = Distribution(["A", "B"], [0.3, 0.7])
plot([p, q])
def test_direction(self):
p = Distribution(["A", "B", "C"], [0.3, 0.3, 0.4])
q = Distribution(["A", "B"], [0.3, 0.7])
self.assertEqual(direction(p, q), -direction(q, p))
self.assertEqual(direction(p, p), 0)
def test_insert(self):
d = Distribution()
self.assertEqual(d.size(), 0)
d.insert("A", 0.5)
d.insert("B", 0.5)
self.assertEqual(d.size(), 2)
def test_append(self):
p = Distribution(["A", "B", "C"], [0.2, 0.25, 0.55])
p.append("A", 0.3)
self.assertEqual(p["A"], 0.5)
def test_normalize(self):
d = Distribution(["A", "B"], [3, 3])
self.assertEqual(d.normalize(), 6)
self.assertEqual(d.getNormalizingConstant(), 6)
self.assertEqual(d["A"], 0.5)
def test_plot(self):
d = Distribution(["A", "B"], [0.3, 0.7])
d.plot(show=False)
def test_getData(self):
d = Distribution(["A", "B", "C"], [0.4, 0.3, 0.2])
keys, values = d.getData()
self.assertEqual(keys, ["A", "B", "C"])
self.assertEqual(values, [0.4, 0.3, 0.2])
def test_append(self):
p = Distribution(["A", "B", "C"], [0.2, 0.25, 0.55])
p.append("A", 0.3)
self.assertEqual(p["A"], 0.5)
def test_str(self):
d = Distribution(["A", "B"], [0.3, 0.7])
self.assertEqual(str(d), '{"A": 0.3, "B": 0.7}')
def test_plot(self):
d = Distribution(["A", "B"], [0.3, 0.7])
d.plot(show=False)
def test_entropy(self):
d = Distribution(["A", "B"], [0.5, 0.5])
assert_allclose(d.entropy(), [1])
def test_getData(self):
d = Distribution(["A", "B", "C"], [0.4, 0.3, 0.2])
keys, values = d.getData()
self.assertEqual(keys, ["A", "B", "C"])
self.assertEqual(values, [0.4, 0.3, 0.2])
def test_insert(self):
d = Distribution()
self.assertEqual(d.size(), 0)
d.insert("A", 0.5)
d.insert("B", 0.5)
self.assertEqual(d.size(), 2)
def test_jsd(self):
p = Distribution(["A", "B"], [0.5, 0.5])
q = Distribution(["A", "C"], [0.5, 0.5])
assert_allclose(jsd(p, 0.5, q, 0.5), [0.5])
def test_entropy(self):
d = Distribution(["A", "B"], [0.5, 0.5])
assert_allclose(d.entropy(), [1])
def test_normalize(self):
d = Distribution(["A", "B"], [3, 3])
self.assertEqual(d.normalize(), 6)
self.assertEqual(d.getNormalizingConstant(), 6)
self.assertEqual(d["A"], 0.5)
def test_contains(self):
d = Distribution(["A", "B"], [0.3, 0.7])
self.assertTrue(d.contains("A"))
self.assertFalse(d.contains("C"))
def test_isEmpty(self):
d = Distribution(["A", "B"], [0.3, 0.7])
self.assertFalse(d.isEmpty())
d = Distribution()
self.assertTrue(d.isEmpty())
def test_contains(self):
d = Distribution(["A", "B"], [0.3, 0.7])
self.assertTrue(d.contains("A"))
self.assertFalse(d.contains("C"))
def test_iadd(self):
p = Distribution(["A", "B", "C"], [0.2, 0.25, 0.55])
q = Distribution(["A"], [0.2])
p += q
self.assertEqual(p["A"], 0.4)